Records with Keyword: Particle Swarm Optimization
Showing records 1 to 25 of 136. [First] Page: 1 2 3 4 5 Last
Multi-Objective Optimization of Drilling GFRP Composites Using ANN Enhanced by Particle Swarm Algorithm
Mohamed S. Abd-Elwahed
September 21, 2023 (v1)
Keywords: artificial neural network, drilling process, glass fiber reinforced polymer, Optimization, Particle Swarm Optimization, response surface analysis, sustainable machining
This paper aims to optimize the quality characteristics of the drilling process in glass fiber-reinforced polymer (GFRP) composites. It focuses on optimizing the drilling parameters with drill point angles concerning delamination damage and energy consumption, simultaneously. The effects of drilling process parameters on machinability were analyzed by evaluating the machinability characteristics. The cutting power was modeled through drilling parameters (speed and feed), drill point angle, and laminate thickness. The response surface analysis and artificial neural networks enhanced by the particle swarm optimization algorithm were applied for modeling and evaluating the effect of process parameters on the machinability of the drilling process. The most influential parameters on machinability properties and delamination were determined by analysis of variance (ANOVA). A multi-response optimization was performed to optimize drilling process parameters for sustainable drilling quality cha... [more]
Research on Path Planning and Tracking Control of Autonomous Vehicles Based on Improved RRT* and PSO-LQR
Yong Zhang, Feng Gao, Fengkui Zhao
July 13, 2023 (v1)
Keywords: autonomous vehicle, linear quadratic regulator, Particle Swarm Optimization, path planning, RRT*, tracking control
Path planning and tracking control are essential parts of autonomous vehicle research. Regarding path planning, the Rapid Exploration Random Tree Star (RRT*) algorithm has attracted much attention due to its completeness. However, the algorithm still suffers from slow convergence and high randomness. Regarding path tracking, the Linear Quadratic Regulator (LQR) algorithm is widely used in various control applications due to its efficient stability and ease of implementation. However, the relatively empirical selection of its weight matrix can affect the control effect. This study suggests a path planning and tracking control framework for autonomous vehicles based on an upgraded RRT* and Particle Swarm Optimization Linear Quadratic Regulator (PSO-LQR) to address the abovementioned issues. Firstly, according to the driving characteristics of autonomous vehicles, a variable sampling area is used to limit the generation of random sampling points, significantly reducing the number of itera... [more]
Research on Valve Life Prediction Based on PCA-PSO-LSSVM
Mingjiang Shi, Peipei Tan, Liansheng Qin, Zhiqiang Huang
June 13, 2023 (v1)
Keywords: ball valve, least squares support vector machine, life prediction, Particle Swarm Optimization, principal component analysis
The valve is a key control component in the oil and gas transportation system, which, due to the environment, transmission medium, and other factors, is susceptible to internal leakage, resulting in valve failure. Conventional testing methods cannot judge the service life of valves. Therefore, it is important to carry out valve life prediction research for oil and gas transmission safety. In this work, a valve service life prediction method based on the PCA-PSO-LSSVM algorithm is proposed. The main factors affecting valve service life are obtained by principal component analysis (PCA), the least squares support vector machine (LSSVM) is used to predict the valve service life, the parameters are optimized by using particle swarm optimization (PSO), and the valve service life prediction model is established. The results show that the predicted valve service life based on the PCA-PSO-LSSVM algorithm is closer to the actual value, with an average relative error (MRE) of 16.57% and a root m... [more]
Prediction in Catalytic Cracking Process Based on Swarm Intelligence Algorithm Optimization of LSTM
Juan Hong, Wende Tian
June 7, 2023 (v1)
Subject: Optimization
Keywords: catalytic cracking process, cuckoo search, long short-term memory network, Particle Swarm Optimization, prediction
Deep learning can realize the approximation of complex functions by learning deep nonlinear network structures, characterizing the distributed representation of input data, and demonstrating the powerful ability to learn the essential features of data sets from a small number of sample sets. A long short-term memory network (LSTM) is a deep learning neural network often used in research, which can effectively extract the dependency relationship between time series data. The LSTM model has many problems such as excessive reliance on empirical settings for network parameters, as well as low model accuracy and weak generalization ability caused by human parameter settings. Optimizing LSTM through swarm intelligence algorithms (SIA-LSTM) can effectively solve these problems. Group behavior has complex behavioral patterns, which makes swarm intelligence algorithms exhibit strong information exchange capabilities. The particle swarm optimization algorithm (PSO) and cuckoo search (CS) algorit... [more]
Optimized Fuel Economy Control of Power-Split Hybrid Electric Vehicle with Particle Swarm Optimization
Hsiu-Ying Hwang, Jia-Shiun Chen
April 25, 2023 (v1)
Subject: Optimization
Keywords: fuel economy, hybrid electric vehicle, Particle Swarm Optimization, power-split
This research focused on real-time optimization control to improve the fuel consumption of power-split hybrid electric vehicles. Particle swarm optimization (PSO) was implemented to reduce fuel consumption for real-time optimization control. The engine torque was design-variable to manage the energy distribution of dual energy sources. The AHS II power-split hybrid electric system was used as the powertrain system. The hybrid electric vehicle model was built using Matlab/Simulink. The simulation was performed according to US FTP-75 regulations. The PSO design objective was to minimize the equivalent fuel rate with the driving system still meeting the dynamic performance requirements. Through dynamic vehicle simulation and PSO, the required torque value for the whole drivetrain system and corresponding high-efficiency engine operating point can be found. With that, the two motor/generators (M/Gs) supplemented the rest required torques. The composite fuel economy of the PSO algorithm was... [more]
Method for Diagnosing a Short-Circuit Fault in the Stator Winding of a Motor Based on Parameter Identification of Features and a Support Vector Machine
Hisahide Nakamura, Yukio Mizuno
April 25, 2023 (v1)
Keywords: diagnosis, parameter identification, Particle Swarm Optimization, short-circuit fault, support vector machine
Motors are widely used in various industrial fields as key power sources, and their importance is increasing. According to the failure occurrence rates of the parts in an electric motor, a short-circuit fault of the winding due to the deterioration of the insulation is among the most probable. An easy and effective method for diagnosing faults is needed to ensure the working condition of a motor with high reliability. This paper proposes a novel method for diagnosing a slight turn-to-turn short-circuit fault in a stator winding that involves an impulse test, parameter identification, and diagnosis. In this work, impulse tests were conducted; the measured voltage characteristics are discussed. Next, the parameter identification of the coefficients of the equivalent circuit of the impulse test was performed using particle swarm optimization. Finally, diagnosis was performed based on a support vector machine that has high classification ability, and the effectiveness of the proposed metho... [more]
Application of Surrogate Optimization Routine with Clustering Technique for Optimal Design of an Induction Motor
Aswin Balasubramanian, Floran Martin, Md Masum Billah, Osaruyi Osemwinyen, Anouar Belahcen
April 24, 2023 (v1)
Subject: Optimization
Keywords: Box–Behnken design, clustering, induction motors, Latin-hypercube sampling, Particle Swarm Optimization, pattern search, surrogate optimization
This paper proposes a new surrogate optimization routine for optimal design of a direct on line (DOL) squirrel cage induction motor. The geometry of the motor is optimized to maximize its electromagnetic efficiency while respecting the constraints, such as output power and power factor. The routine uses the methodologies of Latin-hypercube sampling, a clustering technique and a Box−Behnken design for improving the accuracy of the surrogate model while efficiently utilizing the computational resources. The global search-based particle swarm optimization (PSO) algorithm is used for optimizing the surrogate model and the pattern search algorithm is used for fine-tuning the surrogate optimal solution. The proposed surrogate optimization routine achieved an optimal design with an electromagnetic efficiency of 93.90%, for a 7.5 kW motor. To benchmark the performance of the surrogate optimization routine, a comparative analysis was carried out with a direct optimization routine that uses a fi... [more]
Power Maximization and Turbulence Intensity Management through Axial Induction-Based Optimization and Efficient Static Turbine Deployment
Mfon Charles, David T. O. Oyedokun, Mqhele Dlodlo
April 24, 2023 (v1)
Subject: Optimization
Keywords: artificial bee colony, axial induction, differential evolution, hexagonal layouts, Particle Swarm Optimization, regular layouts, turbulence intensity, wind plant power maximization
Layout optimization is capable of increasing turbine density and reducing wake effects in wind plants. However, such optimized layouts do not guarantee fixed T-2-T distances in any direction and would be disadvantageous if reduction in computational costs due to turbine set-point updates is also a priority. Regular turbine layouts are considered basic because turbine coordinates can be determined intuitively without the application of any optimization algorithms. However, such layouts can be used to intentionally create directions of large T-2-T distances, hence, achieve the gains of standard/non-optimized operations in these directions, while also having close T-2-T distances in other directions from which the gains of optimized operations can be enjoyed. In this study, a regular hexagonal turbine layout is used to deploy turbines within a fixed area dimension, and a turbulence intensity-constrained axial induction-based plant-wide optimization is carried out using particle swarm, art... [more]
Robust Multi-Step Predictor for Electricity Markets with Real-Time Pricing
Sachin Kahawala, Daswin De Silva, Seppo Sierla, Damminda Alahakoon, Rashmika Nawaratne, Evgeny Osipov, Andrew Jennings, Valeriy Vyatkin
April 24, 2023 (v1)
Keywords: demand response, electricity price forecasting, Particle Swarm Optimization, prosumers, real-time pricing
Real-time electricity pricing mechanisms are emerging as a key component of the smart grid. However, prior work has not fully addressed the challenges of multi-step prediction (Predicting multiple time steps into the future) that is accurate, robust and real-time. This paper proposes a novel Artificial Intelligence-based approach, Robust Intelligent Price Prediction in Real-time (RIPPR), that overcomes these challenges. RIPPR utilizes Variational Mode Decomposition (VMD) to transform the spot price data stream into sub-series that are optimized for robustness using the particle swarm optimization (PSO) algorithm. These sub-series are inputted to a Random Vector Functional Link neural network algorithm for real-time multi-step prediction. A mirror extension removal of VMD, including continuous and discrete spaces in the PSO, is a further novel contribution that improves the effectiveness of RIPPR. The superiority of the proposed RIPPR is demonstrated using three empirical studies of mul... [more]
Optimal Allocation of Large-Capacity Distributed Generation with the Volt/Var Control Capability Using Particle Swarm Optimization
Donghyeon Lee, Seungwan Son, Insu Kim
April 20, 2023 (v1)
Subject: Optimization
Keywords: distributed generation, Particle Swarm Optimization, Volt/Var control
Widespread interest in environmental issues is growing. Many studies have examined the effect of distributed generation (DG) from renewable energy resources on the electric power grid. For example, various studies efficiently connect growing DG to the current electric power grid. Accordingly, the objective of this study is to present an algorithm that determines DG location and capacity. For this purpose, this study combines particle swarm optimization (PSO) and the Volt/Var control (VVC) of DG while regulating the voltage magnitude within the allowable variation (e.g., ±5%). For practical optimization, the PSO algorithm is enhanced by applying load profile data (e.g., 24-h data). The objective function (OF) in the proposed PSO method considers voltage variations, line losses, and economic aspects of deploying large-capacity DG (e.g., installation costs) to transmission networks. The case studies validate the proposed method (i.e., optimal allocation of DG with the capability of VVC wi... [more]
Wind Farm Power Optimization and Fault Ride-Through under Inter-Turn Short-Circuit Fault
Kuichao Ma, Mohsen Soltani, Amin Hajizadeh, Jiangsheng Zhu, Zhe Chen
April 20, 2023 (v1)
Subject: Optimization
Keywords: inter-turn short-circuit, Particle Swarm Optimization, power dispatch, wake effect, wind energy, wind farm control
Inter-Turn Short Circuit (ITSC) fault in stator winding is a common fault in Doubly-Fed Induction Generator (DFIG)-based Wind Turbines (WTs). Improper measures in the ITSC fault affect the safety of the faulty WT and the power output of the Wind Farm (WF). This paper combines derating WTs and the power optimization of the WF to diminish the fault effect. At the turbine level, switching the derating strategy and the ITSC Fault Ride-Through (FRT) strategy is adopted to ensure that WTs safely operate under fault. At the farm level, the Particle Swarm Optimization (PSO)-based active power dispatch strategy is used to address proper power references in all of the WTs. The simulation results demonstrate the effectiveness of the proposed method. Switching the derating strategy can increase the power limit of the faulty WT, and the ITSC FRT strategy can ensure that the WT operates without excessive faulty current. The PSO-based power optimization can improve the power of the WF to compensate f... [more]
Swarm Intelligence-Based Methodology for Scanning Electron Microscope Image Segmentation of Solid Oxide Fuel Cell Anode
Maciej Chalusiak, Weronika Nawrot, Szymon Buchaniec, Grzegorz Brus
April 20, 2023 (v1)
Subject: Materials
Keywords: anode, electron tomography, FIB-SEM, image filtering, image processing, microstructure, Particle Swarm Optimization, segmentation, solid oxide fuel cell
Segmentation of images from scanning electron microscope, especially multiphase, poses a drawback in their microstructure quantification process. The labeling process must be automatized due to the time consumption and irreproducibility of the manual labeling procedure. Here we show a swarm intelligence-driven filtration methodology performed on raw solid oxide fuel cell anode’s material images to improve the segmentation methods’ performance. The methodology focused on two significant parts of the segmentation process, which are filtering and labeling. During the first one, the images underwent filtering by applying a series of filters, whose operation parameters were determined using Particle Swarm Optimization upon a dedicated cost function. Next, Seeded Region Growing, k-Means Clustering, Multithresholding, and Simple Linear Iterative Clustering Superpixel algorithms were utilized to label the filtered images’ regions into consecutive phases in the microstructure. The improvement w... [more]
One-Day-Ahead Hourly Wind Power Forecasting Using Optimized Ensemble Prediction Methods
Chao-Ming Huang, Shin-Ju Chen, Sung-Pei Yang, Hsin-Jen Chen
April 18, 2023 (v1)
Subject: Optimization
Keywords: ensemble method, Particle Swarm Optimization, salp swarm algorithm, whale optimization algorithm, wind power forecasting
This paper proposes an optimal ensemble method for one-day-ahead hourly wind power forecasting. The ensemble forecasting method is the most common method of meteorological forecasting. Several different forecasting models are combined to increase forecasting accuracy. The proposed optimal ensemble method has three stages. The first stage uses the k-means method to classify wind power generation data into five distinct categories. In the second stage, five single prediction models, including a K-nearest neighbors (KNN) model, a recurrent neural network (RNN) model, a long short-term memory (LSTM) model, a support vector regression (SVR) model, and a random forest regression (RFR) model, are used to determine five categories of wind power data to generate a preliminary forecast. The final stage uses an optimal ensemble forecasting method for one-day-ahead hourly forecasting. This stage uses swarm-based intelligence (SBI) algorithms, including the particle swarm optimization (PSO), the sa... [more]
Equivalent Modeling of LVRT Characteristics for Centralized DFIG Wind Farms Based on PSO and DBSCAN
Ning Zhou, Huan Ma, Junchao Chen, Qiao Fang, Zhe Jiang, Changgang Li
April 18, 2023 (v1)
Subject: Optimization
Keywords: density-based spatial clustering of applications, doubly-fed induction generator, dynamic equivalence, low voltage ride through, Particle Swarm Optimization
As large-scale wind turbines are connected to the grid, modeling studies of wind farms are essential to the power system dynamic research. Due to the large number of wind turbines in the wind farm, detailed modeling of each wind turbine leads to high model complexity and low simulation efficiency. An equivalent modeling method for the wind farm is needed to reduce the complexity. For wind farms with widely used doubly-fed induction generators (DFIGs), the existing equivalent studies mainly focus on such continuous control parts as electrical control. These methods are unsuitable for the low voltage ride through (LVRT) part which is discontinuous due to switching control. Based on particle swarm optimization (PSO) and density-based spatial clustering of applications (DBSCAN), this paper proposes an equivalent method for LVRT characteristics of wind farms. Firstly, the multi-turbine equivalent model of the wind farm is established. Each wind turbine in the model represents a cluster of w... [more]
Design and Optimization of Linear Permanent Magnet Vernier Generator for Direct Drive Wave Energy Converter
Mei Zhao, Zhiquan Kong, Pingpeng Tang, Zhentao Zhang, Guodong Yu, Huaqiang Zhang, Yongxiang Xu, Jibin Zou
April 17, 2023 (v1)
Keywords: linear motors, Particle Swarm Optimization, permanent magnet vernier generator, response surface model, wave power generation system
A novel linear permanent magnet vernier generator (LPMVG) for small-power off-grid wave power generation systems is proposed in this paper. Firstly, in order to reduce the cogging force and the inherent edge effect of the linear generator, a staggered tooth modular structure is proposed. Secondly, in order to improve the output power and efficiency of the LPMVG and reduce the fluctuation coefficient of electromagnetic force, the relationship between the parameters of the generator is studied, and a method combining multi-objective optimization and single parameter scanning based on the response surface model and particle swarm optimization algorithm is proposed to obtain the optimal structural parameters of the generator. Thirdly, the output power and efficiency of the optimized generator are calculated and analyzed based on the two-dimensional finite element method, and the effectiveness of the multi-objective optimization design method based on the response surface model and particle... [more]
Experts versus Algorithms? Optimized Fuzzy Logic Energy Management of Autonomous PV Hybrid Systems with Battery and H2 Storage
Lisa Gerlach, Thilo Bocklisch
April 14, 2023 (v1)
Subject: Optimization
Keywords: autonomous PV hybrid system, energy management, fuzzy logic control, hybrid energy storage, Particle Swarm Optimization
Off-grid applications based on intermittent solar power benefit greatly from hybrid energy storage systems consisting of a battery short-term and a hydrogen long-term storage path. An intelligent energy management is required to balance short-, intermediate- and long-term fluctuations in electricity demand and supply, while maximizing system efficiency and minimizing component stress. An energy management was developed that combines the benefits of an expert-knowledge based fuzzy logic approach with a metaheuristic particle swarm optimization. Unlike in most existing work, interpretability of the optimized fuzzy logic controller is maintained, allowing the expert to evaluate and adjust it if deemed necessary. The energy management was tested with 65 1-year household load datasets. It was shown that the expert tuned controller is more robust to changes in load pattern then the optimized controller. However, simple readjustments restore robustness, while largely retaining the benefits ac... [more]
Optimal Siting and Sizing of Battery Energy Storage: Case Study Seventh Feeder at Nakhon Phanom Substation in Thailand
Panyawoot Boonluk, Sirote Khunkitti, Pradit Fuangfoo, Apirat Siritaratiwat
April 14, 2023 (v1)
Keywords: battery energy storage systems, distribution networks, optimal siting and sizing, Particle Swarm Optimization, state of energy
The optimal siting and sizing of battery energy storage system (BESS) is proposed in this study to improve the performance of the seventh feeder at Nakhon Phanom substation, which is a distribution network with the connected photovoltaic (PV) in Thailand. The considered objective function aims to improve the distribution network performance by minimizing costs incurred in the distribution network within a day, comprising of voltage regulation cost, real power loss cost, and peak demand cost. Particle swarm optimization (PSO) is applied to solve the optimization problem. It is found that the optimal siting and sizing of the BESS installation could improve the performance of the distribution network in terms of cost minimization, voltage profile, real power loss, and peak demand. The results are investigated from three cases where case 1 is without PV and BESS installation, case 2 is with only PV installation, and case 3 is with PV and BESS installations. The comparison results show that... [more]
An Optimized and Decentralized Energy Provision System for Smart Cities
Ayusee Swain, Surender Reddy Salkuti, Kaliprasanna Swain
April 14, 2023 (v1)
Subject: Optimization
Keywords: advanced metering infrastructure, bio-inspired algorithms, blockchain, Ethereum, Genetic Algorithm, microgrid, Particle Swarm Optimization, wireless sensor network
Energy efficiency and data security of smart grids are one of the major concerns in the context of implementing modern approaches in smart cities. For the intelligent management of energy systems, wireless sensor networks and advanced metering infrastructures have played an essential role in the transformation of traditional cities into smart communities. In this paper, a smart city energy model is proposed in which prosumer communities were built by interconnecting energy self-sufficient households to generate, consume and share clean energy on a decentralized trading platform by integrating blockchain technology with a smart microgrid. The efficiency and stability of the grid network were improved by using several wireless sensor nodes that manage a massive amount of data in the network. However, long communication distances between sensor nodes and the base station can greatly consume the energy of sensors and decrease the network lifespan. Therefore, bio-inspired algorithm approach... [more]
High-Accuracy Power Quality Disturbance Classification Using the Adaptive ABC-PSO as Optimal Feature Selection Algorithm
Supanat Chamchuen, Apirat Siritaratiwat, Pradit Fuangfoo, Puripong Suthisopapan, Pirat Khunkitti
April 14, 2023 (v1)
Keywords: artificial bee colony, optimal feature selection, Particle Swarm Optimization, power quality disturbance classification, probabilistic neural network
Power quality disturbance (PQD) is an important issue in electrical distribution systems that needs to be detected promptly and identified to prevent the degradation of system reliability. This work proposes a PQD classification using a novel algorithm, comprised of the artificial bee colony (ABC) and the particle swarm optimization (PSO) algorithms, called “adaptive ABC-PSO” as the feature selection algorithm. The proposed adaptive technique is applied to a combination of ABC and PSO algorithms, and then used as the feature selection algorithm. A discrete wavelet transform is used as the feature extraction method, and a probabilistic neural network is used as the classifier. We found that the highest classification accuracy (99.31%) could be achieved through nine optimally selected features out of all 72 extracted features. Moreover, the proposed PQD classification system demonstrated high performance in a noisy environment, as well as the real distribution system. When comparing the... [more]
Optimal Pricing of Vehicle-to-Grid Services Using Disaggregate Demand Models
Charilaos Latinopoulos, Aruna Sivakumar, John W. Polak
April 13, 2023 (v1)
Subject: Optimization
Keywords: demand-side management, discrete choice theory, electric vehicle charging, genetic algorithms, Particle Swarm Optimization, revenue management, vehicle-to-grid
The recent revolution in electric mobility is both crucial and promising in the coordinated effort to reduce global emissions and tackle climate change. However, mass electrification brings up new technical problems that need to be solved. The increasing penetration rates of electric vehicles will add an unprecedented energy load to existing power grids. The stability and the quality of power systems, especially on a local distribution level, will be compromised by multiple vehicles that are simultaneously connected to the grid. In this paper, the authors propose a choice-based pricing algorithm to indirectly control the charging and V2G activities of electric vehicles in non-residential facilities. Two metaheuristic approaches were applied to solve the optimization problem, and a comparative analysis was performed to evaluate their performance. The proposed algorithm would result in a significant revenue increase for the parking operator, and at the same time, it could alleviate the o... [more]
The Optimal Placement and Sizing of Distributed Generation in an Active Distribution Network with Several Soft Open Points
Eshan Karunarathne, Jagadeesh Pasupuleti, Janaka Ekanayake, Dilini Almeida
April 13, 2023 (v1)
Keywords: active distribution network, distributed generation, optimal planning, Particle Swarm Optimization, soft open points
A competent methodology based on the active power loss reduction for optimal placement and sizing of distributed generators (DGs) in an active distribution network (ADN) with several soft open points (SOPs) is proposed. A series of SOP combinations are explored to generate different network structures and they are utilized in the optimization framework to identify the possible solutions with minimum power loss under normal network conditions. Furthermore, a generalized methodology to optimize the size and the location of a predefined number of DGs with a predefined number of SOPs is presented. A case study on the modified IEEE 33 bus system with three DGs and five SOPs was conducted and hence the overall network power loss and the voltage improvement were examined. The findings reveal that the system loss of the passive network without SOPs and DGs is reduced by 79.5% using three DGs and five SOPs. In addition, this research work introduces a framework using the DG size and the impedan... [more]
State Estimation-Based Distributed Energy Resource Optimization for Distribution Voltage Regulation in Telemetry-Sparse Environments Using a Real-Time Digital Twin
Rachid Darbali-Zamora, Jay Johnson, Adam Summers, C. Birk Jones, Clifford Hansen, Chad Showalter
April 13, 2023 (v1)
Keywords: digital twin, distributed energy resources, distribution system, Particle Swarm Optimization, photovoltaics, power hardware-in-the-loop, state estimation, voltage regulation
Real-time state estimation using a digital twin can overcome the lack of in-field measurements inside an electric feeder to optimize grid services provided by distributed energy resources (DERs). Optimal reactive power control of DERs can be used to mitigate distribution system voltage violations caused by increased penetrations of photovoltaic (PV) systems. In this work, a new technology called the Programmable Distribution Resource Open Management Optimization System (ProDROMOS) issued optimized DER reactive power setpoints based-on results from a particle swarm optimization (PSO) algorithm wrapped around OpenDSS time-series feeder simulations. This paper demonstrates the use of the ProDROMOS in a RT simulated environment using a power hardware-in-the-loop PV inverter and in a field demonstration, using a 678 kW PV system in Grafton (MA, USA). The primary contribution of the work is demonstrating a RT digital twin effectively provides state estimation pseudo-measurements that can be... [more]
An Optimized PV Control System Based on the Emperor Penguin Optimizer
Mariam A. Sameh, Mostafa I. Marei, M. A. Badr, Mahmoud A. Attia
April 13, 2023 (v1)
Subject: Optimization
Keywords: cuttlefish algorithm, duty cycle, emperor penguin optimizer, maximum power point tracking, partial shading condition, Particle Swarm Optimization, photovoltaic
During the day, photovoltaic (PV) systems are exposed to different sunlight conditions in addition to partial shading (PS). Accordingly, maximum power point tracking (MPPT) techniques have become essential for PV systems to secure harvesting the maximum possible power from the PV modules. In this paper, optimized control is performed through the application of relatively newly developed optimization algorithms to PV systems under Partial Shading (PS) conditions. The initial value of the duty cycle of the boost converter is optimized for maximizing the amount of power extracted from the PV arrays. The emperor penguin optimizer (EPO) is proposed not only to optimize the initial setting of duty cycle but to tune the gains of controllers used for the boost converter and the grid-connected inverter of the PV system. In addition, the performance of the proposed system based on the EPO algorithm is compared with another newly developed optimization technique based on the cuttlefish algorithm... [more]
ELM-QR-Based Nonparametric Probabilistic Prediction Method for Wind Power
Honghai Niu, Yu Yang, Lingchao Zeng, Yiguo Li
April 13, 2023 (v1)
Subject: Optimization
Keywords: comprehensive performance evaluation index, ELM-QR, extreme learning machine, nonparametric probabilistic prediction, Particle Swarm Optimization, quantile regression, wind power forecasting
Wind power has significant randomness. Probabilistic prediction of wind power is necessary to solve the problem of safe and stable power grid dispatching with the integration of large-scale wind power. Therefore, this paper proposes a novel nonparametric probabilistic prediction model for wind power based on extreme learning machine-quantile regression (ELM-QR). Firstly, the ELM-QR models of multiple quantiles are established, and then the new comprehensive index (NCI) is optimized by particle swarm optimization (PSO) to obtain the weighting coefficients corresponding to the lower and upper bounds of the prediction intervals. The final prediction interval is obtained by integrating the outputs of ELM-QR models and the weighting coefficients. Finally, case studies are carried out with the real wind farm operation data, simulation results show that the proposed algorithm can obtain narrower prediction intervals while ensuring high reliability. Through sensitivity analysis and comparison... [more]
A Fault Diagnosis Mechanism with Power Generation Improvement for a Photovoltaic Module Array
Kuei-Hsiang Chao, Pei-Lun Lai
April 13, 2023 (v1)
Keywords: digital signal processor, maximum power point tracking, online diagnostic mechanism, Particle Swarm Optimization, photovoltaic module array
This paper aims to develop an online diagnostic mechanism, doubling as a maximum power point tracking scheme, for a photovoltaic (PV) module array. In case of malfunction or shadow event occurring to a PV module, the presented diagnostic mechanism is enabled, automatically and immediately, to reconfigure a PV module array for maximum output power operation under arbitrary working conditions. Meanwhile, the malfunctioning or shaded PV module can be located instantly by this diagnostic mechanism according to the array configuration, and a PV module replacement process is made more efficient than ever before for the maintenance crew. In this manner, the intended maximum output power operation can be resumed as soon as possible in consideration of a minimum business loss. Using a particle swarm optimization (PSO)-based algorithm, the PV module array is reconfigured by means of switch manipulations between modules, such that a load is supplied with the maximum amount of output power. For co... [more]
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